Most laboratories studying biological processes and human disease use microscopes to image cells or other biological samples. Even for small-scale experiments, the information sought from images is increasingly quantitative and complex, and automated microscopes collect images faster than can be examined by eye. We will continue our development of CellProfiler (www.cellprofiler.org) to meet this strong and growing demand for software to analyze biological images. CellProfiler is a versatile, open-source toolbox. Using its point-and-click interface, researchers build a customized workflow of image-analysis modules to identify and measure biological objects in images. It can extract valuable biological information from images quickly, even for high-throughput experiments, while increasing objectivity and statistical power in microscopy experiments. Published only seven years ago, CellProfiler is already an important and widely used tool: it is launched 100,000+ times per year by users around the world and has been cited in more than 800 papers from 600 distinct laboratories. The software evolves in an intensely collaborative and interdisciplinary research environment with dozens of ongoing projects and has been successfully applied to a wide range of biological samples and assays, from counting cells to scoring complex phenotypes by machine learning. We propose to improve CellProfiler's capabilities in order to enable further biological imaging research, meet the needs of the growing user base, and expand the community that benefits from CellProfiler: First, microscopy experiments are rapidly expanding in both scale and scope, and are beginning to push CellProfiler's limits, particularly when they involve larger images (e.g., for tissue samples, cellular microarrays, large field-of-view cameras), multi-dimensional images (time-lapse and three-dimensional imaging), images for morphological profiling, and novel microscopy types (super-resolution and single-molecule imaging). We will upgrade CellProfiler's capabilities to serve these needs and add proven, state-of-the-art algorithms for image processing (especially segmentation and filtering for non-fluorescence images), time-lapse and 3D analysis, and novel microscopy types. We will also add features and usability improvements requested by the large CellProfiler community. Second, we will enable researchers to create sophisticated bioimaging analysis workflows by expanding CellProfiler's interoperability with complementary software (e.g., MATLAB, ImageJ, MicroManager, KNIME). Third, we will disseminate CellProfiler and provide user, educator, and developer support. There is great demand for our online forum, downloadable materials, and in-person tutorials (1,000+ attendees so far). These improvements to the first, and still preeminent, open-source software for modular, high- throughput biological image analysis will enable hundreds of NIH-funded laboratories to make high-impact biological discoveries from microscopy images across all disciplines within biology.